Jake Ryland Williams

Computer Vision and Pattern Recognition · 2014–2016

A researcher in the Science corpus: 2 papers; trusts 6 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

Whom Jake Ryland Williams trusts

Their strongest outbound trust, strongest first, inferred from their citations.

  1. Karen Simonyan (in Computer Vision and Pattern Recognition)
  2. Andrew Zisserman (in Computer Vision and Pattern Recognition)
  3. Alex Krizhevsky (in Computer Vision and Pattern Recognition)
  4. Geoffrey E. Hinton (in Computer Vision and Pattern Recognition)
  5. Ilya Sutskever (in Computer Vision and Pattern Recognition)
  6. Rainer Lienhart (in Computer Vision and Pattern Recognition)

Papers from Jake Ryland Williams's perspective

Ordered by how strongly the researchers Jake Ryland Williams trusts endorse each paper. Same corpus, a different order for every researcher.

  1. ImageNet classification with deep convolutional neural networks
  2. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
  3. Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation
  4. Building high-level features using large scale unsupervised learning
  5. Attribute and simile classifiers for face verification
  6. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
  7. Gradient-based learning applied to document recognition
  8. Deep Learning
  9. Long Short-Term Memory
  10. Attention is All you Need (Neural Information Processing Systems)

Their papers